AIMC Topic: Metagenome

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De novo Nanopore read quality improvement using deep learning.

BMC bioinformatics Nov 6, 2019
BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often c...
Humans Quality Improvement Metagenome Nanopore Sequencing Software Deep Learning Databases, Genetic
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Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.

Biology direct Aug 20, 2019
BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacteria...
Biomarkers Cities Metabolome Machine Learning Metagenomics Metagenome Drug Resistance, Microbial Microbiota
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Application of machine learning techniques for creating urban microbial fingerprints.

Biology direct Aug 16, 2019
BACKGROUND: Research has found that human associated microbial communities play a role in homeostasis and the disruption of these communities may be important in an array of medical conditions. However outside of the human body many of these communit...
Cities Machine Learning Microbiota Metagenome Metagenomics DNA Fingerprinting
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Massive metagenomic data analysis using abundance-based machine learning.

Biology direct Aug 1, 2019
BACKGROUND: Metagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms th...
Metagenomics Metagenome Data Analysis Microbiota Machine Learning
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Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance.

IEEE transactions on nanobioscience Apr 24, 2019
High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomi...
Microbiota Pharynx High-Throughput Nucleotide Sequencing Databases, Genetic Decision Trees Metagenomics Metagenome Humans Algorithms Phylogeny Support Vector Machine
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MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.) Mar 16, 2019
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology...
Metagenomics Metagenome Diabetes Mellitus, Type 2 Obesity Algorithms Microbiota Deep Learning Humans Machine Learning
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Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics Nov 5, 2018
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...
Humans Models, Statistical Supervised Machine Learning Metformin Metagenome Gastrointestinal Microbiome Neural Networks, Computer Databases, Genetic Computational Biology Causality
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Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection.

IEEE/ACM transactions on computational biology and bioinformatics Sep 13, 2018
To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial s...
Diagnosis, Computer-Assisted Feces Bacteria Gastrointestinal Microbiome Metagenome Colorectal Neoplasms Male Middle Aged Female Aged Neural Networks, Computer Machine Learning Humans Algorithms
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Deep learning models for bacteria taxonomic classification of metagenomic data.

BMC bioinformatics Jul 9, 2018
BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria cla...
Models, Genetic Neural Networks, Computer RNA, Ribosomal, 16S Algorithms Time Factors Metagenomics Databases, Genetic Metagenome Bacteria Reproducibility of Results Deep Learning
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Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics Oct 4, 2017
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...
Metagenome Phenotype Humans Cluster Analysis Machine Learning Metagenomics
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